---
title: Agentic RAG with LanceDB
---

This example demonstrates how to implement Agentic RAG using LanceDB vector database with OpenAI embeddings, enabling the agent to search and retrieve relevant information dynamically.

## Code

```python agentic_rag_lancedb.py
"""
1. Run: `pip install openai lancedb tantivy pypdf sqlalchemy agno` to install the dependencies
2. Run: `python cookbook/rag/04_agentic_rag_lancedb.py` to run the agent
"""

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.lancedb import LanceDb, SearchType

knowledge = Knowledge(
    # Use LanceDB as the vector database and store embeddings in the `recipes` table
    vector_db=LanceDb(
        table_name="recipes",
        uri="tmp/lancedb",
        search_type=SearchType.vector,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

knowledge.add_content(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    knowledge=knowledge,
    # Add a tool to search the knowledge base which enables agentic RAG.
    # This is enabled by default when `knowledge` is provided to the Agent.
    search_knowledge=True,
    markdown=True,
)
agent.print_response(
    "How do I make chicken and galangal in coconut milk soup", stream=True
)
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install libraries">
    ```bash
    pip install -U agno openai lancedb tantivy pypdf sqlalchemy
    ```
  </Step>

  <Step title="Export your OpenAI API key">

    <CodeGroup>

    ```bash Mac/Linux
      export OPENAI_API_KEY="your_openai_api_key_here"
    ```

    ```bash Windows
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
    ```
    </CodeGroup> 
  </Step>

  <Step title="Create a Python file">
    Create a Python file and add the above code.
    ```bash
    touch agentic_rag_lancedb.py
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
    ```bash Mac
    python agentic_rag_lancedb.py
    ```
    
    ```bash Windows
    python agentic_rag_lancedb.py
    ```
    </CodeGroup>
  </Step>

  <Step title="Find All Cookbooks">
  Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:

  <Link href="https://github.com/agno-agi/agno/tree/main/cookbook/agents/rag" target="_blank">
    Agno Cookbooks on GitHub
  </Link>
</Step>
</Steps>